As the desire to improve energy efficiency has become more widespread, a need for effective methods and infrastructure to monitor and control the consumption of energy by buildings of all sizes has become of increasing importance, and for large buildings in particular. As a result, building management systems have become increasingly common. Building management systems typically provide for the measurement and control of building energy systems, including the measurement of consumption of various energy-related resources by building systems including for example electrical energy, natural gas, thermal energy, steam energy, or water usage, through the use of electronic and/or computerized building energy meters. Such building energy meters then provide building energy consumption data to an automated or computer controlled building management system.
In addition to the adoption of automated building management systems to measure and control building systems and building energy consumption, in order to improve building energy efficiency, it has also become of interest to provide predicted or expected typical values for building energy loads for a particular building, in order to enable more accurate and efficient planning, management, and fault detection of energy consumption. To that end, multiple approaches have been made towards the determination of predicted or typical building energy load values for a building, which have been adapted to attempt to take into account the effect of various parameters that may influence building energy consumption, and therefore the building energy load values that are predicted.
Certain of the known approaches to determining predicted or typical values for building energy load have been based on the use of detailed physical building models to calculate the predicted or typical building energy load values for given environmental or predictor variables such as temperature, humidity, wind, occupancy, etc. Such detailed physical building models may typically model heating and cooling behavior and associated energy consumption for a building by constructing energy consumption models for the building including consideration of many building-specific design parameters, such as dimensions, materials, orientation, window coverage, insulation, HVAC systems, and lighting, for example, in addition to consideration of the effect of external environmental variables such as temperature, humidity and wind, for example. However, such physical building models may be very complex, and as a result typically require extensive building-specific evaluation and analysis of design factors by skilled professionals to produce accurate models. Therefore, the use of such physical building model methods to generate predicted or typical building energy load values may be impractical for use on multiple dissimilar buildings, due to the computational and economic cost of developing such models for each building. This approach may also be unsuitable for use in connection with buildings for which detailed design data is not available or impractical to obtain.
Other known approaches to determining predicted or typical values for building energy loads have been based on numerical modeling tools that attempt to relate previously measured building energy load values and associated variations in external environmental variables such as temperature, humidity and wind, to provide predicted or typical building energy load values corresponding to observed or predicted environmental variables. One such approach makes use of linear or higher order polynomial functions that attempt to fit measured historical building energy load values to measured values of environmental variables, after which the resulting polynomial model may be applied to environmental variable values for the predicted conditions, to obtain a predicted or typical value for building energy loads. However, models based on such polynomial functions may not provide a good fit to building energy load data, in particular low order polynomials may be unable to follow step changes and higher order polynomials may overfit to noise. Also, the use of polynomial functions may result in poor estimated or predicted values for variables outside the range of training datasets.
Other known numerical modeling approaches are based on the use of neural networks to fit historical building energy load and predictor variable data for a building, to enable prediction of building energy load values for given predictor variable value inputs. However, the use of neural network techniques may require complex computation of many parameters in order to fit or learn from a historical building energy load dataset. This approach may further be prone to unstable or erroneous prediction behavior when used to produce predicted building energy load values corresponding to predictor variable values which are outside the range of the historical dataset used to fit or train the neural network system. Also, the use of neural network techniques may typically involve the use of a large number of parameters relative to their expressive power, which may contribute to estimation or prediction errors related to overfitting in some applications, depending upon the size and/or range of the historical building energy load dataset available to use in training or initializing of a neural network model.
Accordingly, a need exists for improved methods and systems for building energy monitoring such as may be desirably adaptable for use with multiple buildings and without the requirement of extensive computational resources.